1. Field
This application relates generally to human-computer interfaces, and more specifically to a system, article of manufacture and method for determining user engagement and sentiment with learned models and user-facing camera images.
2. Related Art
Currently, user engaging with digital media content can be determined through such metrics as ‘click through’ rate, number of uploads, amount of time an advertisement was allowed to completely run, etc. However these metrics can be deficient with respect to determining whether the user actually paid attention (e.g., did the user even view the digital media content, how did the user react and where did they mostly focus while watching the content?) Moreover, current methods can use external eye-tracking hardware. This can limit the benefits of eye tracking to particular users with access to the external eye-tracking hardware. Consequently, improvements are sought that can determine user engagement and sentiment with learned models and user-facing camera images